SoroushVahidi/scidocs huggingface dataset:
SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis
(2014)

This paper describes our SeemGo system for the task of Aspect Based Sentiment Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that obtains the F-score of 0.683 for Laptops and 0.791 for Restaurants by exploiting both word-based features and context features. The other three subtasks are solved by the Maximum Entropy model, with the occurrence counts of unigram and bigram words of each sentence as features. The subtask of aspect category detection obtains the best result when applying the Boosting method on the Maximum Entropy model, with the precision of 0.869 for Restaurants. The Maximum Entropy model also shows good performance in the subtasks of both aspect term and aspect category polarity classification.
Machine learning approach for automated screening of malaria parasite using light microscopic images.
(2013)

The aim of this paper is to address the development of computer assisted malaria parasite characterization and classification using machine learning approach based on light microscopic images of peripheral blood smears. In doing this, microscopic image acquisition from stained slides, illumination correction and noise reduction, erythrocyte segmentation, feature extraction, feature selection and finally classification of different stages of malaria (Plasmodium vivax and Plasmodium falciparum) have been investigated. The erythrocytes are segmented using marker controlled watershed transformation and subsequently total ninety six features describing shape-size and texture of erythrocytes are extracted in respect to the parasitemia infected versus non-infected cells. Ninety four features are found to be statistically significant in discriminating six classes. Here a feature selection-cum-classification scheme has been devised by combining F-statistic, statistical learning techniques i.e., Bayesian learning and support vector machine (SVM) in order to provide the higher classification accuracy using best set of discriminating features. Results show that Bayesian approach provides the highest accuracy i.e., 84% for malaria classification by selecting 19 most significant features while SVM provides highest accuracy i.e., 83.5% with 9 most significant features. Finally, the performance of these two classifiers under feature selection framework has been compared toward malaria parasite classification.
Multi-factor Authentication Security Framework in Cloud Computing
(2015)

Data Security is the most critical issues in a cloud computing environment. Authentication is a key technology for information security, which is a mechanism to establish proof of identities to get access of information in the system. Traditional password authentication does not provide enough security for information in cloud computing environment to the most modern means of attacks. In this paper, we propose a new multi-factor authentication framework for cloud computing. In this paper the features of various access control mechanisms are discussed and a novel framework of access control is proposed for cloud computing, which provides a multi -step and multifactor authentication of a user. The model proposed is well-organized and provably secure solution of access control for externally hosted applications.
A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms
(2016)

Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.
Top 10 algorithms in data mining
(2007)

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.
Rapid Object Detection using a Boosted Cascade of Simple Features
(2001)

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.
An Associative Classification Data Mining Approach for Detecting Phishing Websites
(2013)

Phishing websites are fake websites that are created by dishonest people to mimic webpages of real websites. Victims of phishing attacks may expose their financial sensitive information to the attacker whom might use this information for financial and criminal activities. Various approaches have been proposed to detect phishing websites, among which, approaches that utilize data mining techniques had shown to be more effective. The main goal of data mining is to analyze a large set of data to identify unsuspected relation and extract understandable useful patterns. Associative Classification (AC) is a promising data mining approach that integrates association rule and classification to build classification models (classifiers). This paper, proposes a new AC algorithm called Phishing Associative Classification (PAC), for detecting phishing websites. PAC employed a novel methodology in construction the classifier which results in generating moderate size classifiers. The algorithm improved the effectiveness and efficiency of a known algorithm called MCAR, by introducing a new prediction procedure and adopting a different rule pruning procedure. The conducted experiments compared PAC with 4 well-known data mining algorithms, these are: covering algorithm (Prism), decision tree (C4.5), associative Classification (CBA) and MCAR. Experiments are performed on a dataset that consists of 1010 website. Each Website is represented using 17 features categorized into 4 sets. The features are extracted from the website contents and URL. The results on each features set show that PAC is either equivalent or more effective than the compared algorithms. When all features are considered, PAC outperformed the compared algorithms and correctly identified 99.31% of the tested websites. Furthermore, PAC produced less number of rules than MCAR, and therefore, is more efficient.
Using Stories to Teach Human Values to Artificial Agents
(2016)

Value alignment is a property of an intelligent agent indicating that it can only pursue goals that are beneficial to humans. Successful value alignment should ensure that an artificial general intelligence cannot intentionally or unintentionally perform behaviors that adversely affect humans. This is problematic in practice since it is difficult to exhaustively enumerated by human programmers. In order for successful value alignment, we argue that values should be learned. In this paper, we hypothesize that an artificial intelligence that can read and understand stories can learn the values tacitly held by the culture from which the stories originate. We describe preliminary work on using stories to generate a value-aligned reward signal for reinforcement learning agents that prevents psychotic-appearing behavior.
Supervised and Unsupervised methods to detect Insider Threat from Enterprise Social and Online Activity Data
(2015)

Insider threat is a significant security risk for organizations, and detection of insider threat is of paramount concern to organizations. In this paper, we attempt to discover insider threat by analyzing enterprise social and online activity data of employees. To this end, we process and extract relevant features that are possibly indicative of insider threat behavior. This includes features extracted from social data including email communication patterns and content, and online activity data such as web browsing patterns, email frequency, and file and machine access patterns. Subsequently, we take two approaches to detect insider threat: (i) an unsupervised approach where we identify statistically abnormal behavior with respect to these features using state-of-the-art anomaly detection methods, and (ii) a supervised approach where we use labels indicating when employees quit the company as a proxy for insider threat activity to design a classifier. We test our approach on a real world data set with artificially injected insider threat events. We obtain a ROC score of 0.77 for the unsupervised approach, and a classification accuracy of 73.4% for the supervised approach. These results indicate that our proposed approaches are fairly successful in identifying insider threat events. Finally, we build a visualization dashboard that enables managers and HR personnel to quickly identify employees with high threat risk scores which will enable them to take suitable preventive measures and limit security risk.
An analytical framework for data stream mining techniques based on challenges and requirements
(2011)

A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples. The imminent need for turning such data into useful information and knowledge augments the development of systems, algorithms and frameworks that address streaming challenges. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. Generally, two main challenges are designing fast mining methods for data streams and need to promptly detect changing concepts and data distribution because of highly dynamic nature of data streams. The goal of this article is to analyze and classify the application of diverse data mining techniques in different challenges of data stream mining. In this paper, we present the theoretical foundations of data stream analysis and propose an analytical framework for data stream mining techniques.
BLIS: A Framework for Rapidly Instantiating BLAS Functionality
(2015)

The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).
 Replicated Computational Results Certified: This article was subjected to an optional supplementary review process in which the computational results leading to the main conclusions of the paper were independently replicated. A separate report with the results of this process is published in this issue (see: http://dx.doi.org/10.1145/2738033). The certification process is described in Michael Heroux's editorial: http://dx.doi.org/10.1145/2743015.
Video2GIF: Automatic Generation of Animated GIFs from Video
(2016)

We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video that really capture our attention. GIFs tell a story, express emotion, turn events into humorous moments, and are the new wave of photojournalism. We pose the question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content? We propose a Robust Deep RankNet that, given a video, generates a ranked list of its segments according to their suitability as GIF. We train our model to learn what visual content is often selected for GIFs by using over 100K user generated GIFs and their corresponding video sources. We effectively deal with the noisy web data by proposing a novel adaptive Huber loss in the ranking formulation. We show that our approach is robust to outliers and picks up several patterns that are frequently present in popular animated GIFs. On our new large-scale benchmark dataset, we show the advantage of our approach over several state-of-the-art methods.
SoilJ : An ImageJ Plugin for the Semiautomatic Processing of Three-Dimensional X-ray Images of Soils
(2018)

Noninvasive threeand four-dimensional X-ray imaging approaches have proved to be valuable analysis tools for vadose zone research. One of the main bottlenecks for applying X-ray imaging to data sets with a large number of soil samples is the relatively large amount of time and expertise needed to extract quantitative data from the respective images. SoilJ is a plugin for the free and open imaging software ImageJ that aims at automating the corresponding processing steps for cylindrical soil columns. It includes modules for automatic column outline recognition, correction of image intensity bias, image segmentation, extraction of particulate organic matter and roots, soil surface topography detection, as well as morphology and percolation analyses. In this study, the functionality and precision of some key SoilJ features were demonstrated on five different image data sets of soils. SoilJ has proved to be useful for strongly decreasing the amount of time required for image processing of large image data sets. At the same time, it allows researchers with little experience in image processing to make use of X-ray imaging methods. The SoilJ source code is freely available and may be modified and extended at will by its users. It is intended to stimulate further community-driven development of this software.
Trade-Offs in PMU Deployment for State Estimation in Active Distribution Grids
(2012)

Monitoring systems are expected to play a major role in active distribution grids, and the design of the measurement infrastructure is a critical element for an effective operation. The use of any available and newly installed, though heterogeneous, metering device providing more accurate and real-time measurement data offers a new paradigm for the distribution grid monitoring system. In this paper the authors study the meter placement problem for the measurement infrastructure of an active distribution network, where heterogeneous measurements provided by Phasor Measurement Units (PMUs) and other advanced measurement systems such as Smart Metering systems are used in addition to measurements that are typical of distribution networks, in particular substation measurements and a-priori knowledge. This work aims at defining a design approach for finding the optimal measurement infrastructure for an active distribution grid. The design problem is posed in terms of a stochastic optimization with the goal of bounding the overall uncertainty of the state estimation using heterogeneous measurements while minimizing the investment cost. The proposed method is also designed for computational efficiency so to cover a wide set of scenarios.
Relative clustering validity criteria: A comparative overview
(2010)

Many different relative clustering validity criteria exist that are very useful in practice as quantitative measures for evaluating the quality of data partitions, and new criteria have still been proposed from time to time. These criteria are endowed with particular features that may make each of them able to outperform others in specific classes of problems. In addition, they may have completely different computational requirements. Then, it is a hard task for the user to choose a specific criterion when he or she faces such a variety of possibilities. For this reason, a relevant issue within the field of clustering analysis consists of comparing the performances of existing validity criteria and, eventually, that of a new criterion to be proposed. In spite of this, the comparison paradigm traditionally adopted in the literature is subject to some conceptual limitations. The present paper describes an alternative, possibly complementary methodology for comparing clustering validity criteria and uses it to make an extensive comparison of the performances of 40 criteria over a collection of 962,928 partitions derived from five well-known clustering algorithms and 1080 different data sets of a given class of interest. A detailed review of the relative criteria under investigation is also provided that includes an original comparative asymptotic analysis of their computational complexities. This work is intended to be a complement of the classic study reported in 1985 by Milligan and Cooper as well as a thorough extension of a preliminary paper by the authors themselves.  2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3:
A multi-resonant gate driver for Very-High-Frequency (VHF) resonant converters
(2017)

This paper presents the design and implementation of a Very-High-Frequency (VHF) multi-resonant gate drive circuit. The design procedure is greatly simplified compared with some VHF self-oscillating multi-resonant gate drivers presented in previous works. The proposed circuit has the potential to reduce long start-up time required in a self-oscillating resonant gate drive circuit and to better utilize fast transient capability of VHF converters. A prototype resonant gate driver is demonstrated and able to reduce 60 % of the gate driving loss of a 20 MHz 32 W Class-E power amplifier with Si MOSFET.
Automated segmentation of cell nuclei in PAP smear images
(2006)

In this paper an automated method for cell nucleus segmentation in PAP smear images is presented. The method combines the global knowledge about the cells and nuclei appearance and the local characteristics of the area of the nuclei, in order to achieve an accurate nucleus boundary. Filters and morphological operators in all three channels of a color image result in the determination of the locations of nuclei in the image, even in cases where cell overlapping occurs. The nucleus boundary is determined by a deformable model. The results are very promising, even when images with high degree of overlapping are used.
Two-Stream Neural Networks for Tampered Face Detection
(2017)

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swaping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectness of our method.
Orestes: A scalable Database-as-a-Service architecture for low latency
(2014)

Today, the applicability of database systems in cloud environments is considerably restricted because of three major problems: I) high network latencies for remote/mobile clients, II) lack of elastic horizontal scalability mechanisms, and III) missing abstraction of storage and data models. In this paper, we propose an architecture, a REST/HTTP protocol and a set of algorithms to solve these problems through a Database-as-a-Service middleware called Orestes (Objects RESTfully Encapsulated in Standard Formats). Orestes exposes cloud-hosted NoSQL database systems through a scalable tier of REST servers. These provide database-independent, object-oriented schema design, a client-independent REST-API for database operations, globally distributed caching, cache consistency mechanisms and optimistic ACID transactions. By comparative evaluations we offer empirical evidence that the proposed Database-as-a-Service architecture indeed solves common latency, scalability and abstraction problems encountered in modern cloud-based applications.